03-01_TablesAndFigures
Yan-Cheng Lin
2023-11-09
Tables and Figures
library(ggplot2)
library(RColorBrewer)
library(magrittr)
library(wesanderson)
load("../Data/Results.RData")Data
Wilcox Test
PA_wilcoxHaplotype Statistics
HBStat_sumPrediction Accuracy
CV_sumFV2 Composition
FV2CompTables
Mantel correlation
Man_mat## FixedHB HaploView HaploBlocker
## DH_KE 0.9955470 0.9800637 0.9298979
## DH_PE 0.9956359 0.9798048 0.9411741
## GC_KE 0.9921593 0.9657868 0.9440275
## GC_PE 0.9953509 0.9717126 0.9842589Wilcoxon test of prediction accuracy (scenario1)
sign_mat <- matrix(nrow = 3, ncol = 3,
dimnames = list(c("FixedHB", "HaploView", "HaploBlocker"),
c("Decreased", "Increased", "Equal")))
for (m in c("FixedHB", "HaploView", "HaploBlocker")){
PA_wilcox_sub <- subset(PA_wilcox, HBMethod == m )
sign_mat[ m, "Decreased"] <- with(PA_wilcox_sub,
sum(Difference < 0 & pvalue_bon < 0.05))
sign_mat[ m, "Increased"] <- with(PA_wilcox_sub,
sum(Difference > 0 & pvalue_bon < 0.05))
sign_mat[ m, "Equal"] <- with(PA_wilcox_sub,
sum(pvalue_bon > 0.05))
}
sign_mat## Decreased Increased Equal
## FixedHB 0 10 10
## HaploView 0 10 10
## HaploBlocker 4 6 10Number of haplotypes (dimention reduction)
nHB_Table <- cbind(
subset(HBStat_sum, select = nHB_filter,
Scenario == "Scenario1" &
HBMethod == "FixedHB" &
Param1 == "win20"),
subset(HBStat_sum, select = nHB_filter,
Scenario == "Scenario1" &
HBMethod == "HaploView" &
Param1 == "GAB"),
subset(HBStat_sum, select = nHB_filter,
Scenario == "Scenario1" &
HBMethod == "HaploBlocker" &
Param1 == "win20" &
Param2 == "tc99")
) %>%
set_colnames(c("FixedHB", "HaploView", "HaploBlocker")) %>%
set_rownames(c("DH_KE", "DH_PE", "GC_KE", "GC_PE"))
print(nHB_Table)## FixedHB HaploView HaploBlocker
## DH_KE 84406 51860 10225
## DH_PE 106620 77314 14236
## GC_KE 112092 72215 53178
## GC_PE 127420 93445 41239Figures
Haplotype Charateristics
# Subset Data #
dat_plot <- subset(HBStat_sum,
Scenario == "Scenario1")
# Plot #
ggplot(data = subset(dat_plot, HBMethod == "FixedHB"),
aes(x = nMR_Mean, y = nHB_filter/1000, group = Train, color = Train)) +
geom_point(shape = 4, size = 4) +
geom_line() +
theme_bw(base_size = 20) +
scale_x_continuous(breaks = c(5,10,20,50,100)) +
labs(title = "FixedHB",
x = "Haplotype Length (SNPs)",
y = "Total Number of Alleles (K)",
color = "Population") +
scale_color_manual(values = c(wes_palette(name="Darjeeling1")[c(1,3,5)],
wes_palette(name="Rushmore")[c(3)])) 
# Subset Data #
dat_plot <- subset(HBStat_sum,
Scenario == "Scenario1")
# Plot #
ggplot(data = subset(dat_plot, HBMethod == "HaploView"),
aes(x = nMR_Mean, y = nHB_filter/1000, group = Train, color = Train, shape = Param1)) +
geom_point(size = 4) +
labs(title = "HaploView",
x = "Haplotype Length (SNPs)",
y = "Total Number of Alleles (K)",
color = "Population",
shape = "Algorithm") +
theme_bw(base_size = 20) +
scale_shape_manual(values = 16:18,
labels = c("4GAM", "GABRIEL", "SPINE")) +
scale_color_manual(values = c(wes_palette(name="Darjeeling1")[c(1,3,5)],
wes_palette(name="Rushmore")[c(3)])) 
# Subset Data #
dat_plot <- subset(HBStat_sum,
Scenario == "Scenario1")
# Plot #
ggplot(data = subset(dat_plot, HBMethod == "HaploBlocker"),
aes(x = nMR_Mean, y = nHB_filter/1000, group = Train, color = Train)) +
geom_point(size = 3) +
labs(title = "HaploBlocker",
x = "Haplotype Length (SNPs)",
y = "Total Number of Alleles (K)",
color = "Population") +
theme_bw(base_size = 20) +
scale_color_manual(values = c(wes_palette(name="Darjeeling1")[c(1,3,5)],
wes_palette(name="Rushmore")[c(3)])) 
Prediction Accuracy (SNP vs HB)
# Scenario 1 #
# Select the optimal haplotype sets #
sel <- with(CV_sum, (Scenario == "Scenario1") &
((HBMethod == "FixedHB" & Param1 == "win20")|
(HBMethod == "HaploView" & Param1 == "GAB")|
(HBMethod == "HaploBlocker" & Param1 == "win20" & Param2 == "tc99"))
)
dat_plot <- CV_sum[ sel,]
dat_plot_snp <- subset(CV_sum,
Scenario == "Scenario1" & HBMethod == "SNP")
key <- match(paste(dat_plot$Trait, dat_plot$Train),
paste(dat_plot_snp$Trait, dat_plot_snp$Train))
dat_plot$PA_SNP <- dat_plot_snp[key, "PA"]
dat_plot$HBMethod <- factor(dat_plot$HBMethod,
levels = c("FixedHB", "HaploView", "HaploBlocker"))
# Fig_scen1_SNPvsHB #
ggplot(data = dat_plot, aes(x = PA_SNP, y = PA, color = Valid, shape = Trait)) +
geom_abline(slope = 1, intercept = 0) +
geom_point(size = 3) +
scale_shape_manual(values = c(8, 15:18)) +
facet_grid(cols = vars(HBMethod)) +
theme_bw(base_size = 20) +
labs(title = "Scenario 1",
x = "Prediction Accuracy (SNP)",
y = "Prediction Accuracy (HB)",
color = "Population", shape = "Trait") +
scale_color_manual(values = c(wes_palette(name="Darjeeling1")[c(1,3,5)],
wes_palette(name="Rushmore")[c(3)])) 
# Scenario 2 #
# Select the optimal haplotype sets #
sel <- with(CV_sum, (Scenario == "Scenario2") &
((HBMethod == "FixedHB" & Param1 == "win20")|
(HBMethod == "HaploView" & Param1 == "GAB")|
(HBMethod == "HaploBlocker" & grepl(Train, pattern = "KE") &
Param1 == "MinSubgroup40" & Param2 == "MCMB1")|
(HBMethod == "HaploBlocker" & grepl(Train, pattern = "PE") &
Param1 == "MinSubgroup40" & Param2 == "MCMB1250"))
)
dat_plot <- CV_sum[ sel,]
dat_plot_snp <- subset(CV_sum,
Scenario == "Scenario2" & HBMethod == "SNP")
key <- match(paste(dat_plot$Trait, dat_plot$Train),
paste(dat_plot_snp$Trait, dat_plot_snp$Train))
dat_plot$PA_SNP <- dat_plot_snp[key, "PA"]
dat_plot$HBMethod <- factor(dat_plot$HBMethod,
levels = c("FixedHB", "HaploView", "HaploBlocker"))
# Fig_scen2_SNPvsHB #
ggplot(data = dat_plot, aes(x = PA_SNP, y = PA, color = Valid, shape = Trait)) +
geom_abline(slope = 1, intercept = 0) +
geom_point(size = 3) +
scale_shape_manual(values = c(8, 15:18)) +
facet_grid(cols = vars(HBMethod)) +
theme_bw(base_size = 20) +
labs(title = "Scenario 2",
x = "Prediction Accuracy (SNP)",
y = "Prediction Accuracy (HB)",
color = "Population", shape = "Trait") +
scale_color_manual(values = c(wes_palette(name="Darjeeling1")[c(1,3,5)],
wes_palette(name="Rushmore")[c(3)])) 
# Scenario 3 #
# Select the optimal haplotype sets #
sel <- with(CV_sum, (Scenario == "Scenario3") &
((HBMethod == "FixedHB" & Param1 == "win10")|
(HBMethod == "HaploView" & Param1 == "GAB")|
(HBMethod == "HaploBlocker" & grepl(Train, pattern = "DH") &
Param1 == "MinSubgroup20" & Param2 == "MCMB1250")|
(HBMethod == "HaploBlocker" & grepl(Train, pattern = "GC") &
Param1 == "MinSubgroup0" & Param2 == "MCMB1"))
)
dat_plot <- CV_sum[ sel,]
dat_plot_snp <- subset(CV_sum,
Scenario == "Scenario3" & HBMethod == "SNP")
key <- match(paste(dat_plot$Trait, dat_plot$Train),
paste(dat_plot_snp$Trait, dat_plot_snp$Train))
dat_plot$PA_SNP <- dat_plot_snp[key, "PA"]
dat_plot$HBMethod <- factor(dat_plot$HBMethod,
levels = c("FixedHB", "HaploView", "HaploBlocker"))
dat_plot$DHorGC <- substr(dat_plot$Train, start = 1, stop = 2)
# Fig_scen3_SNPvsHB #
ggplot(data = dat_plot, aes(x = PA_SNP, y = PA, color = Valid, shape = Trait)) +
geom_abline(slope = 1, intercept = 0) +
geom_point(size = 4) +
scale_shape_manual(values = c(8, 15:18)) +
facet_grid(rows = vars(DHorGC), cols = vars(HBMethod)) +
theme_bw(base_size = 20) +
labs(title = "Scenario 3",
x = "Prediction Accuracy (SNP)",
y = "Prediction Accuracy (HB)",
color = "Prediction Set", shape = "Trait")+
scale_color_manual(values = c(wes_palette(name="Darjeeling1")[c(1,3,5)],
wes_palette(name="Rushmore")[c(3)])) 
Impact of Parameters
Scenario 1
HaploBlocker
dat_plot <- subset(HBStat_sum,
Scenario == "Scenario1" &
HBMethod == "HaploBlocker" &
!Param2 == "NoTC")
dat_plot$Param1 <- factor(dat_plot$Param1,
levels = c("win0", "win5", "win10", "win20", "win50", "win100"
),
labels = c("MultiWin", "5", "10", "20", "50", "100"))
dat_plot$Param2 <- factor(dat_plot$Param2,
levels = c("tc80", "tc85", "tc90", "tc95", "tc99"),
labels = c("80", "85", "90", "95", "99"))
cols <- c("MultiWin" = "#4DAF4A", # green
"5" = brewer.pal(8, "RdYlBu")[8],
"10" = brewer.pal(8, "RdYlBu")[7],
"20" = brewer.pal(8, "RdYlBu")[6],
"50" = brewer.pal(8, "RdYlBu")[2],
"100" = brewer.pal(8, "RdYlBu")[1]
)
## Params vs Length (HaploBlocker) ##
ggplot(data = dat_plot, aes(x = Param2, y = nMR_Mean, color = Param1, group = paste(Param1, Train))) +
geom_line() +
geom_point() +
labs(title = "HaploBlocker (scenario 1)",
x = "Target Coverage",
y = "Haplotype Length (SNPs)",
color = "Window Size") +
facet_grid(rows = vars(Train), scale = "free") +
theme_bw(base_size = 20) +
theme(axis.text.x=element_text(angle = -45, hjust = 0)) +
scale_color_manual(values = cols)
# Data preparation #
dat_plot <- subset(CV_sum,
Scenario == "Scenario1" &
HBMethod == "HaploBlocker" &
!Param2 == "NoTC")
dat_plot <- aggregate(data = dat_plot,
PA ~ Train + Param1 + Param2,
mean)
dat_plot$Param1 <- factor(dat_plot$Param1,
levels = c("win0", "win5", "win10", "win20", "win50", "win100"
),
labels = c("MultiWin", "5", "10", "20", "50", "100"))
dat_plot$Param2 <- factor(dat_plot$Param2,
levels = c("tc80", "tc85", "tc90", "tc95", "tc99"),
labels = c("80", "85", "90", "95", "99"))
## Params vs PA (HaploBlocker) ##
ggplot(data = dat_plot, aes(x = Param2, y = PA, color = Param1, group = paste(Param1, Train))) +
geom_line() +
geom_point() +
labs(title = "HaploBlocker (scenario 1)",
x = "Target Coverage",
y = "Prediction Accuracy",
color = "Window Size") +
facet_grid(rows = vars(Train), scales = "free") +
theme_bw(base_size = 20) +
theme(axis.text.x=element_text(angle = -45, hjust = 0)) +
scale_color_manual(values = cols)
FixedHB
# Data preparation #
dat_plot <- subset(CV_sum,
Scenario == "Scenario1" & HBMethod == "FixedHB")
dat_plot_snp <- subset(CV_sum,
Scenario == "Scenario1" & HBMethod == "SNP")
dat_plot$Param1 <- factor(dat_plot$Param1,
levels = c("win5", "win10", "win20", "win50", "win100"),
labels = c("5", "10", "20", "50", "100"))
key <- match(paste(dat_plot$Trait, dat_plot$Train),
paste(dat_plot_snp$Trait, dat_plot_snp$Train))
## Params vs PA (FixedHB) ##
ggplot(data = dat_plot,
aes(x = Param1, y = PA, color = Trait, shape = Trait, group = paste(Trait, Train))) +
geom_point(size = 2) +
geom_line() +
geom_abline(data = dat_plot_snp, aes(intercept = PA, slope = 0, color = Trait), linetype = "dashed") +
scale_shape_manual(values=c(8, 15:18)) +
facet_grid(cols = vars(Train), scales = "free_y") +
labs(title = "FixedHB (scenario 1)",
x = "Window Size",
y = "Prediction Accuracy") +
theme_bw(base_size = 20) +
theme(axis.text.x=element_text(angle = -45, hjust = 0)) +
guides(color = guide_legend("Trait"), shape = guide_legend("Trait")) +
scale_color_brewer(palette = "Set1") 
HaploView
# Data preparation #
dat_plot <- subset(CV_sum,
Scenario == "Scenario1" & HBMethod == "HaploView")
dat_plot_snp <- subset(CV_sum,
Scenario == "Scenario1" & HBMethod == "SNP")
dat_plot <- merge(dat_plot, dat_plot_snp,
by = c("Trait", "Train"),
suffixes = c("_HB", "_SNP"))
dat_plot$PA_diff <- dat_plot$PA_HB - dat_plot$PA_SNP
## Params vs PA (HaploView) ##
ggplot(data = dat_plot,
aes(x = Trait, y = PA_diff, fill = Param1_HB, group = Param1_HB)) +
geom_bar(position="dodge", stat="identity") +
facet_grid(cols = vars(Train), scales = "free_y") +
labs(title = "HaploView (scenario 1)",
x = "Methods",
y = "Relative Prediction Accuracy") +
theme_bw(base_size = 20) +
scale_fill_brewer(palette = "Set1", name = "Methods") +
theme(axis.text.x=element_text(angle = -45, hjust = 0))
Scenario 2
HaploBlocker
dat_plot <- subset(HBStat_sum,
Scenario == "Scenario2" &
HBMethod == "HaploBlocker")
dat_plot$Param1 <- factor(dat_plot$Param1,
levels = c("MinSubgroup0", "MinSubgroup5","MinSubgroup20", "MinSubgroup40", "MinSubgroup80", "MinSubgroup160"),
labels = c("0", "5","20", "40", "80", "160"))
dat_plot$Param2 <- factor(dat_plot$Param2,
levels = c("MCMB1", "MCMB1250", "MCMB5000", "MCMB20000", "MCMB80000"),
labels = c("1", "1250","5000", "20000", "80000"))
ggplot(data = dat_plot,
aes(y = nMR_Mean, x = Param2, group = paste(Train, Param1),
shape = Param1, color = Train))+
geom_point(size = 4) +
geom_line() +
labs(title = "HaploBlocker Length (scenario 2)",
x = "MCMB",
y = "Haplotype Length",
color = "Landrace",
shape = "Min Subgroup") +
theme_bw(base_size = 20) +
scale_color_brewer(palette = "Set1") +
guides(color = guide_legend(order = 1)) +
theme(axis.text.x=element_text(angle = -45, hjust = 0))
dat_plot <- subset(HBStat_sum,
Scenario == "Scenario2" &
HBMethod == "HaploBlocker")
dat_plot$Param1 <- factor(dat_plot$Param1,
levels = c("MinSubgroup0", "MinSubgroup5","MinSubgroup20", "MinSubgroup40", "MinSubgroup80", "MinSubgroup160"),
labels = c("0", "5","20", "40", "80", "160"))
dat_plot$Param2 <- factor(dat_plot$Param2,
levels = c("MCMB1", "MCMB1250", "MCMB5000", "MCMB20000", "MCMB80000"),
labels = c("1", "1250","5000", "20000", "80000"))
ggplot(data = dat_plot,
aes(y = GenomeCoverage, x = Param2, group = paste(Train, Param1),
shape = Param1, color = Train))+
geom_point(size = 4) +
geom_line() +
labs(title = "HaploBlocker Coverage (scenario 2)",
x = "MCMB",
y = "Genome Coverage",
color = "Landrace",
shape = "Min Subgroup") +
theme_bw(base_size = 20) +
scale_color_brewer(palette = "Set1") +
guides(color = guide_legend(order = 1)) +
theme(axis.text.x=element_text(angle = -45, hjust = 0))
dat_sub <- subset(CV_sum,
Scenario == "Scenario2" &
HBMethod == "HaploBlocker" &
!Param1 %in% c("MinSubgroup30", "MinSubgroup50"))
dat_sub$Param1 <- factor(dat_sub$Param1,
levels = c("MinSubgroup0", "MinSubgroup5","MinSubgroup20", "MinSubgroup40", "MinSubgroup80", "MinSubgroup160"),
labels = c("0", "5","20", "40", "80", "160"))
dat_sub$Param2 <- factor(dat_sub$Param2,
levels = c("MCMB1", "MCMB1250", "MCMB5000", "MCMB20000", "MCMB80000"),
labels = c("1", "1250","5000", "20000", "80000"))
dat_sub$Pop <- substr(dat_sub$Train, start = 4, stop = 5)
CV_merge <- aggregate(data = dat_sub,
PA ~ Pop + Param1 + Param2,
mean)
dat_plot <- with(data = CV_merge,
data.frame(PA = PA,
Param = c(Param1, Param2),
Pop = Pop,
Group = rep(c("subpop", "MCMB"), each = nrow(CV_merge))))
dat_plot$Group <- factor(dat_plot$Group,
levels = c("MCMB", "subpop"),
labels = c("MCMB", "Min Subgroup"))
ggplot(data = dat_plot, aes(x = Param, y = PA)) +
geom_boxplot() +
facet_grid(cols = vars(Group), rows = vars(Pop), scales = c("free")) +
labs(title = "HaploBlocker Optimization (scenario 2)",
x = "Parameters",
y = "Prediction Accuracy") +
theme_bw(base_size = 20) +
theme(axis.text.x=element_text(angle = -45, hjust = 0)) 
FixedHB
# Data preparation #
dat_plot <- subset(CV_sum,
Scenario == "Scenario2" & HBMethod == "FixedHB")
dat_plot_snp <- subset(CV_sum,
Scenario == "Scenario2" & HBMethod == "SNP")
dat_plot$Param1 <- factor(dat_plot$Param1,
levels = c("win5", "win10", "win20", "win50", "win100"),
labels = c("5", "10", "20", "50", "100"))
key <- match(paste(dat_plot$Trait, dat_plot$Train),
paste(dat_plot_snp$Trait, dat_plot_snp$Train))
## Params vs PA (FixedHB) ##
ggplot(data = dat_plot,
aes(x = Param1, y = PA, color = Trait, shape = Trait, group = paste(Trait, Train))) +
geom_point(size = 2) +
geom_line() +
geom_abline(data = dat_plot_snp, aes(intercept = PA, slope = 0, color = Trait), linetype = "dashed") +
scale_shape_manual(values=c(8, 15:18)) +
facet_grid(cols = vars(Train), scales = "free_y") +
labs(title = "FixedHB (scenario 2)",
x = "Window Size",
y = "Prediction Accuracy") +
theme_bw(base_size = 20) +
theme(axis.text.x=element_text(angle = -45, hjust = 0)) +
guides(color = guide_legend("Trait"), shape = guide_legend("Trait")) +
scale_color_brewer(palette = "Set1") 
Scenario 3
HaploBlocker
dat_plot <- subset(HBStat_sum,
Scenario == "Scenario3" &
HBMethod == "HaploBlocker")
dat_plot$Param1 <- factor(dat_plot$Param1,
levels = c("MinSubgroup0", "MinSubgroup5","MinSubgroup20", "MinSubgroup40", "MinSubgroup80", "MinSubgroup160"),
labels = c("0", "5","20", "40", "80", "160"))
dat_plot$Param2 <- factor(dat_plot$Param2,
levels = c("MCMB1", "MCMB1250", "MCMB5000", "MCMB20000", "MCMB80000"),
labels = c("1", "1250","5000", "20000", "80000"))
ggplot(data = dat_plot,
aes(y = nMR_Mean, x = Param2, group = paste(Train, Param1),
shape = Param1, color = Train))+
geom_point(size = 4) +
geom_line() +
labs(title = "HaploBlocker Length (scenario 3)",
x = "MCMB",
y = "Haplotype Length",
color = "Population",
shape = "Min Subgroup") +
theme_bw(base_size = 20) +
scale_color_brewer(palette = "Set1") +
guides(color = guide_legend(order = 1)) +
theme(axis.text.x=element_text(angle = -45, hjust = 0))
dat_plot <- subset(HBStat_sum,
Scenario == "Scenario3" &
HBMethod == "HaploBlocker")
dat_plot$Param1 <- factor(dat_plot$Param1,
levels = c("MinSubgroup0", "MinSubgroup5","MinSubgroup20", "MinSubgroup40", "MinSubgroup80", "MinSubgroup160"),
labels = c("0", "5","20", "40", "80", "160"))
dat_plot$Param2 <- factor(dat_plot$Param2,
levels = c("MCMB1", "MCMB1250", "MCMB5000", "MCMB20000", "MCMB80000"),
labels = c("1", "1250","5000", "20000", "80000"))
ggplot(data = dat_plot,
aes(y = GenomeCoverage, x = Param2, group = paste(Train, Param1),
shape = Param1, color = Train))+
geom_point(size = 4) +
geom_line() +
labs(title = "HaploBlocker Coverage (scenario 3)",
x = "MCMB",
y = "Genome Coverage",
color = "Population",
shape = "Min Subgroup") +
theme_bw(base_size = 20) +
scale_color_brewer(palette = "Set1") +
guides(color = guide_legend(order = 1)) +
theme(axis.text.x=element_text(angle = -45, hjust = 0))
dat_sub <- subset(CV_sum,
Scenario == "Scenario3" &
HBMethod == "HaploBlocker")
dat_sub$Param1 <- factor(dat_sub$Param1,
levels = c("MinSubgroup0", "MinSubgroup5","MinSubgroup20", "MinSubgroup40", "MinSubgroup80", "MinSubgroup160"),
labels = c("0", "5","20", "40", "80", "160"))
dat_sub$Param2 <- factor(dat_sub$Param2,
levels = c("MCMB1", "MCMB1250", "MCMB5000", "MCMB20000", "MCMB80000"),
labels = c("1", "1250","5000", "20000", "80000"))
dat_sub$Pop <- substr(dat_sub$Train, start = 1, stop = 2)
CV_merge <- aggregate(data = dat_sub,
PA ~ Pop + Param1 + Param2,
mean)
dat_plot <- with(data = CV_merge,
data.frame(PA = PA,
Param = c(Param1, Param2),
Pop = Pop,
Group = rep(c("subpop", "MCMB"), each = nrow(CV_merge))))
dat_plot$Group <- factor(dat_plot$Group,
levels = c("MCMB", "subpop"),
labels = c("MCMB", "Min Subgroup"))
ggplot(data = dat_plot, aes(x = Param, y = PA)) +
geom_boxplot() +
facet_grid(cols = vars(Group), rows = vars(Pop), scales = c("free")) +
labs(title = "HaploBlocker Optimization (scenario 3)",
x = "Parameters",
y = "Prediction Accuracy") +
theme_bw(base_size = 20) +
theme(axis.text.x=element_text(angle = -45, hjust = 0)) 
FixedHB
# Data preparation #
dat_plot <- subset(CV_sum,
Scenario == "Scenario3" & HBMethod == "FixedHB")
dat_plot_snp <- subset(CV_sum,
Scenario == "Scenario3" & HBMethod == "SNP")
dat_plot$Param1 <- factor(dat_plot$Param1,
levels = c("win5", "win10", "win20", "win50", "win100"),
labels = c("5", "10", "20", "50", "100"))
key <- match(paste(dat_plot$Trait, dat_plot$Train),
paste(dat_plot_snp$Trait, dat_plot_snp$Train))
## Params vs PA (FixedHB) ##
ggplot(data = dat_plot,
aes(x = Param1, y = PA, color = Trait, shape = Trait, group = paste(Trait, Train))) +
geom_point(size = 2) +
geom_line() +
geom_abline(data = dat_plot_snp, aes(intercept = PA, slope = 0, color = Trait), linetype = "dashed") +
scale_shape_manual(values=c(8, 15:18)) +
facet_grid(cols = vars(Train), scales = "free_y") +
labs(title = "FixedHB (scenario 3)",
x = "Window Size",
y = "Prediction Accuracy") +
theme_bw(base_size = 20) +
theme(axis.text.x=element_text(angle = -45, hjust = 0)) +
guides(color = guide_legend("Trait"), shape = guide_legend("Trait")) +
scale_color_brewer(palette = "Set1") 
FV2 Component
par(mfrow = c(1,2))
with(data = subset(FV2Comp, Landrace == "KE"),
plot(y = DH_FV2Comp, x = MinSubgroup, type = "b",
main = "DH_KE", ylab = "FV2 Composition", xlab = "Min Subgroup"))
with(data = subset(FV2Comp, Landrace == "PE"),
plot(y = DH_FV2Comp, x = MinSubgroup, type = "b",
main = "DH_PE", ylab = "FV2 Composition", xlab = "Min Subgroup")) 
sel <- with(CV_sum, (Scenario == "Scenario2") &
((HBMethod == "FixedHB" & Param1 == "win20")|
(HBMethod == "HaploView" & Param1 == "GAB")|
(HBMethod == "HaploBlocker" & grepl(Train, pattern = "KE") &
Param1 == "MinSubgroup40" & Param2 == "MCMB1")|
(HBMethod == "HaploBlocker" & grepl(Train, pattern = "PE") &
Param1 == "MinSubgroup40" & Param2 == "MCMB1250"))
)
dat_plot <- subset(CV_sum,
(HBMethod == "HaploBlocker" &
grepl(Train, pattern = "KE") &
Param1 == "MinSubgroup30") |
(HBMethod == "HaploBlocker" &
grepl(Train, pattern = "PE") &
Param1 == "MinSubgroup50")
)
dat_plot_snp <- subset(CV_sum,
Scenario == "Scenario2" & HBMethod == "SNP")
key <- match(paste(dat_plot$Trait, dat_plot$Train),
paste(dat_plot_snp$Trait, dat_plot_snp$Train))
dat_plot$PA_SNP <- dat_plot_snp[key, "PA"]
dat_plot$HBMethod <- factor(dat_plot$HBMethod,
levels = c("FixedHB", "HaploView", "HaploBlocker"))
# Fig_scen2_SNPvsHB #
ggplot(data = dat_plot, aes(x = PA_SNP, y = PA, color = Train, shape = Trait)) +
geom_abline(slope = 1, intercept = 0) +
geom_point(size = 3) +
scale_shape_manual(values = c(8, 15:18)) +
facet_grid(cols = vars(HBMethod)) +
theme_bw(base_size = 20) +
labs(title = "Scenario 2",
x = "Prediction Accuracy (SNP)",
y = "Prediction Accuracy (HB)",
color = "Population", shape = "Trait") +
scale_color_manual(values = c(wes_palette(name="Darjeeling1")[c(1,3,5)],
wes_palette(name="Rushmore")[c(3)])) 
Weighted GRM
dat_plot <- subset(CV_sum, Scenario == "Weight")
dat_plot$Scale <- as.numeric(dat_plot$Scale)
dat_plot_snp <- subset(CV_sum, Scenario == "Scenario1" &
HBMethod == "SNP" &
Train == "DH_PE")
dat_plot_snp$Weight <- NULL; dat_plot_snp$Scale <- NULL
ggplot(data = dat_plot, aes(x = Scale, y = PA)) +
geom_point() +
geom_line() +
geom_hline(data = dat_plot_snp, aes(yintercept = PA), linetype = "dashed") +
facet_grid(rows = vars(Trait), cols = vars(Weight), "free") +
theme_bw(base_size = 20) +
labs(title = "Scenario 1 (DH_PE)",
x = "Scaling Factor (s)",
y = "Prediction Accuracy") +
scale_x_continuous(breaks = seq(0, 2, by = 0.5))